镇江市跑马山滑坡监测及预警预报技术研究

发布时间：2018-03-21 14:19

本文选题：边坡监测　切入点：滑坡预报　出处：《南京大学》2017年硕士论文　论文类型：学位论文

【摘要】：在极端天气增多和人类工程活动加剧等因素的影响下,我国的滑坡地质灾害呈现多发趋势。近年来,江苏省镇江市滑坡地质灾害逐年增多,在东部沿海地区具有一定代表性。本文以镇江市跑马山滑坡灾害防治工程为依托,开展滑坡监测及预警预报等研究工作。论文在系统收集已有研究资料和野外工程地质调查的基础上,系统分析了镇江地区地质环境特征、滑坡灾害及古滑坡再活动特征,重点研究了跑马山山体工程地质特征,通过现场勘探和土工试验,给出边坡岩土体物理力学指标,在此基础上,确定该边坡以表面变形监测为主,并以降雨量监测相结合的监测方案,布设监测点,运用无线网络系统实现全自动远程监测,实时掌握边坡动态变形过程,对这一滑坡地质灾害成功实现了预测预报,取得主要研究成果如下。1.对滑坡变形监测数据中的变异突变数据的分析和处理,并且采用Matlab编程对缺失数据进行插补,为跑马山滑坡变形分析及失稳破坏预测预报提供了连续、可靠的监测数据。2.在分析跑马山地质特征及监测数据的基础上,着重研究了降雨对跑马山滑坡变形破坏的演化规律,并从监测数据中的累计位移量数据中,根据位移速率角分析法判定了跑马山滑坡的变形破坏阶段;在综合分析镇江地区降雨数据和累计位移数据的基础上,结合判定出的跑马山滑坡的变形破坏阶段,得出跑马山变形破坏的机理。3.以蠕变理论为基础,建立斋藤迪孝时序预测模型,通过等速变形阶段应变历时曲线和加速变形阶段应变历时曲线的分析计算,得出滑坡最终破坏时间。4.以动态GM(1,1)模型为基础,开展了跑马山滑坡变形趋势预测模型研究,并通过模拟计算,最终确定最佳数据序列长度,然后校检模型的可信度。5.以非线性灰色时间预测预报模型为基础,展开了边坡整体失稳时间预测预报的模型的研究,将该模型应用于跑马山滑坡整体失稳破坏时间的预测预报研究中。这一研究结果为镇江地区的边坡监测预报与防治提供了依据。
[Abstract]:Under the influence of the increase of extreme weather and the aggravation of human engineering activities, the landslide geological hazards in China show a tendency of frequent occurrence. In recent years, the landslide geological disasters in Zhenjiang City, Jiangsu Province have been increasing year by year. It is representative in the eastern coastal area. This paper relies on the project of preventing and controlling the landslide in Pingma Mountain, Zhenjiang City. Based on the systematic collection of existing research data and field engineering geological survey, the geological environment characteristics, landslide disasters and reactivity characteristics of ancient landslides in Zhenjiang area are systematically analyzed. In this paper, the engineering geological characteristics of the Haimashan mountain body are studied emphatically, and the physical and mechanical indexes of the rock and soil mass of the slope are given through field exploration and geotechnical test. On this basis, it is determined that the monitoring of the surface deformation of the slope is the main part. Based on the monitoring scheme of rainfall monitoring, the monitoring points are set up, the automatic remote monitoring is realized by wireless network system, and the dynamic deformation process of slope is grasped in real time. The prediction of the landslide geological hazard is successfully realized. The main research results are as follows: 1. The analysis and processing of the variation and mutation data in the landslide deformation monitoring data, and the interpolation of missing data by Matlab programming, which provides a continuous method for the deformation analysis and prediction of instability and failure in Happy Valley landslide. 2. On the basis of analyzing the geological characteristics and monitoring data of Haima Mountain, the evolution law of deformation and failure caused by rainfall is studied, and from the accumulative displacement data of monitoring data, Based on the analysis of displacement rate angle analysis, the deformation and failure stage of Heimashan landslide is determined, and on the basis of comprehensive analysis of rainfall data and accumulative displacement data in Zhenjiang area, the deformation and failure stage of the landslide is determined. On the basis of creep theory, the prediction model of Saito Di Xiao time series is established. The strain duration curve of constant velocity deformation stage and acceleration deformation stage are analyzed and calculated. Finally, based on the dynamic GM1 / 1) model, the prediction model of landslide deformation trend is studied, and the optimal length of the data sequence is determined by simulation calculation. Secondly, the reliability of the model is checked. 5. Based on the nonlinear grey time prediction model, the research on the prediction model of the whole slope instability time is carried out. The model is applied to the prediction and prediction of the overall instability and failure time of the slope in Happy Valley, which provides a basis for the slope monitoring, prediction and prevention and control in Zhenjiang area.
【学位授予单位】：南京大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：P642.22

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